AI-Enabled Secure IoT-Based Remote Monitoring and Intelligent Control Framework for Industrial Automation Using Edge Computing
Keywords:
Artificial intelligence, Cybersecurity, Edge computing, Industrial automation, Industrial IoT, Industry 4.0, Internet of ThingsAbstract
The increasing adoption of the Internet of Things (IoT) has significantly improved industrial automation by enabling continuous monitoring, remote operation, and efficient management of industrial equipment. Conventional automation systems often depend on centralized processing, which may lead to higher communication delays, increased network traffic, and reduced system scalability. This paper presents a secure IoT-based framework for remote monitoring and control in industrial automation. The proposed architecture consists of six integrated layers: Perception Layer, Communication Layer, Edge Layer, Cloud Layer, Intelligence Layer, and Application Layer. These layers work together to support data acquisition, communication, local processing, cloud-based data management, operational analysis, and user interaction. Edge computing is employed to process time-sensitive information near industrial devices, thereby reducing latency and improving response time. The framework also incorporates data analysis techniques to support equipment condition monitoring and maintenance planning. To enhance system security, encryption, authentication, and intrusion detection mechanisms are integrated into the communication and cloud infrastructure. The proposed framework applies to smart manufacturing, industrial process monitoring, energy management, and automated production systems. The architecture aims to improve operational efficiency, reduce maintenance costs, enhance system reliability, and provide secure remote access for industrial applications. The proposed framework offers a practical foundation for developing scalable and secure IoT-enabled industrial automation systems. Performance evaluation indicates that the proposed architecture provides lower communication latency, reduced network traffic, improved fault detection capability, and better system scalability than conventional cloud-centric industrial automation approaches. These characteristics make the framework suitable for smart manufacturing environments that require reliable remote monitoring, secure information exchange, and efficient management of industrial assets. The proposed work offers a practical architectural model for developing scalable and secure IIoT applications that support the objectives of Industry 4.0.
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